code: def custom_unfold(input, dimension, size, step):
      """Custom torch.Tensor.unfold implementation to enable the export to ONNX."""
      import torch
  
      shape = input.size()
      rank = len(shape)
      sizedim = shape[dimension]
  
      low_indices = torch.arange(0, sizedim, step)
      min_length = (
          torch.div(sizedim - size, step, rounding_mode="floor") + 1
      )
      indices = torch.arange(size) + low_indices[:min_length][:, None]
  
      s = [slice(None)] * rank
      s[dimension] = indices
      sliced = input[s]
  
      perm = list(range(0, rank + 1))
      perm.append(perm.pop(dimension + 1))
  
      return sliced.permute(perm)
  
file_path: transformers/src/transformers/models/gpt_neo/configuration_gpt_neo.py
imports: from collections import OrderedDict
  from typing import Any, Mapping, Optional
  from ...None import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: custom_unfold(input, dimension, size, step) and returns: sliced